def jacobian(self, U, mu=None):
mu = self.parse_parameter(mu)
options = self.solver_options.get('jacobian') if self.solver_options else None
if len(self.interpolation_dofs) == 0:
if isinstance(self.source, NumpyVectorSpace) and isinstance(self.range, NumpyVectorSpace):
return NumpyMatrixOperator(np.zeros((self.range.dim, self.source.dim)), solver_options=options,
source_id=self.source.id, range_id=self.range.id,
name=self.name + '_jacobian')
else:
return ZeroOperator(self.range, self.source, name=self.name + '_jacobian')
elif hasattr(self, 'operator'):
return EmpiricalInterpolatedOperator(self.operator.jacobian(U, mu=mu), self.interpolation_dofs,
self.collateral_basis, self.triangular,
solver_options=options, name=self.name + '_jacobian')
else:
restricted_source = self.restricted_operator.source
U_dofs = restricted_source.make_array(U.dofs(self.source_dofs))
JU = self.restricted_operator.jacobian(U_dofs, mu=mu) \
.apply(restricted_source.make_array(np.eye(len(self.source_dofs))))
try:
if self.triangular:
interpolation_coefficients = solve_triangular(self.interpolation_matrix, JU.to_numpy().T,
lower=True, unit_diagonal=True).T
else:
interpolation_coefficients = solve(self.interpolation_matrix, JU.to_numpy().T).T
except ValueError: # this exception occurs when AU contains NaNs ...
interpolation_coefficients = np.empty((len(JU), len(self.collateral_basis))) + np.nan
J = self.collateral_basis.lincomb(interpolation_coefficients)
if isinstance(J.space, NumpyVectorSpace):
J = NumpyMatrixOperator(J.to_numpy().T, range_id=self.range.id)
else:
J = VectorArrayOperator(J)
return Concatenation([J, ComponentProjection(self.source_dofs, self.source)],
solver_options=options, name=self.name + '_jacobian')
def jacobian(self, U, mu=None):
mu = self.parse_parameter(mu)
options = self.solver_options.get('jacobian') if self.solver_options else None
if len(self.interpolation_dofs) == 0:
if isinstance(self.source, NumpyVectorSpace) and isinstance(self.range, NumpyVectorSpace):
return NumpyMatrixOperator(np.zeros((self.range.dim, self.source.dim)), solver_options=options,
source_id=self.source.id, range_id=self.range.id,
name=self.name + '_jacobian')
else:
return ZeroOperator(self.range, self.source, name=self.name + '_jacobian')
elif hasattr(self, 'operator'):
return EmpiricalInterpolatedOperator(self.operator.jacobian(U, mu=mu), self.interpolation_dofs,
self.collateral_basis, self.triangular,
solver_options=options, name=self.name + '_jacobian')
else:
restricted_source = self.restricted_operator.source
U_dofs = restricted_source.make_array(U.dofs(self.source_dofs))
JU = self.restricted_operator.jacobian(U_dofs, mu=mu) \
.apply(restricted_source.make_array(np.eye(len(self.source_dofs))))
try:
if self.triangular:
interpolation_coefficients = solve_triangular(self.interpolation_matrix, JU.to_numpy().T,
lower=True, unit_diagonal=True).T
else:
interpolation_coefficients = solve(self.interpolation_matrix, JU.to_numpy().T).T
except ValueError: # this exception occurs when AU contains NaNs ...
interpolation_coefficients = np.empty((len(JU), len(self.collateral_basis))) + np.nan
J = self.collateral_basis.lincomb(interpolation_coefficients)
if isinstance(J.space, NumpyVectorSpace):
J = NumpyMatrixOperator(J.to_numpy().T, range_id=self.range.id)
else:
J = VectorArrayOperator(J)
return Concatenation([J, ComponentProjection(self.source_dofs, self.source)],
solver_options=options, name=self.name + '_jacobian')
